Gen AI Engineer

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We're committed to bringing passion and customer focus to the business.

Job Description: Gen AI Engineer

Responsibilities

• Design, develop, and implement advanced solutions leveraging Large Language Models (LLMs).
• Take full ownership of initiatives, delivering end-to-end solutions with minimal supervision.
• Stay current with the latest advancements in Generative AI, LLMs, RAG systems, and applied research.
• Build and maintain reusable code libraries, tools, and frameworks to accelerate AI development.
• Participate in code reviews to ensure high-quality, maintainable, and scalable solutions.
• Contribute across the entire software development lifecycle—design, implementation, testing, deployment, and maintenance.
• Collaborate with cross-functional teams to align AI solutions with business goals, integrate contributions into core systems, and influence roadmaps.
• Apply strong analytical and problem-solving skills to design efficient solutions for complex business challenges.
• Communicate effectively across technical and non-technical teams, ensuring transparency and alignment.
• Own business impact of AI solutions, including adoption, accuracy, latency, and cost efficiency
• Translate ambiguous business problems into structured AI solution approaches and measurable outcomes
• Drive solution success metrics (e.g., productivity gains, automation %, decision accuracy)
• Engage directly with business and technical stakeholders to understand requirements, present solutions, and influence decision-making
• Communicate solution architecture and trade-offs clearly to both technical and non-technical audiences
• Contribute to client discussions, PoCs, and proposal development.
• Design scalable, modular, and production-grade AI systems (APIs, pipelines, orchestration layers)
• Define architecture patterns for LLM applications (RAG pipelines, agentic workflows, hybrid systems)
• Make trade-offs across latency, cost, accuracy, and maintainability
• Build reusable accelerators, frameworks, and components that can be leveraged across multiple use cases and clients
• Contribute to internal IP creation (assets, templates, reference architectures)
• Ensure reliability and robustness of LLM systems through evaluation frameworks, guardrails, and fallback strategies
• Design safe and responsible AI systems (hallucination mitigation, bias handling, governance)
• Optimize cost-performance trade-offs in large-scale deployments
• Identify when NOT to use LLMs and propose alternative approaches
• Contribute to code reviews, design reviews, and mentorship of junior team members
• Drive quality standards and best practices across projects
• Stay ahead of advancements in GenAI and proactively evaluate their applicability to business problems
• Contribute to internal knowledge sharing, training, and capability building.
• Must-Have Skills

Generative AI & NLP

• SaaS-based LLMs: LangChain, LlamaIndex, vector databases, prompt engineering (CoT, ReAct, agents), Azure OpenAI function calling, multimodal models.
• Open-Source and SaaS LLMs: Azure OpenAI, Claude Opus 4.6, GPT-3.5 Turbo, GPT-4, etc.
• At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, n8n, LangFlow, SmolAgents, Semantic Kernel.
• Advanced Retrieval-Augmented Generation (RAG) systems: hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, evaluation/monitoring of RAG pipelines.
• Classical NLP: text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and Named Entity Recognition (NER).
• Databricks ecosystem: Databricks Genie, Databricks AI/BI, AgentBricks
• MS Copilot Studio and knowledge on no-code/low-code app development.
• MCP server, tools, skills and creation and maintenance of reusable components.
• Tech Stack
• Programming & Frameworks: Python, FastAPI
• Cloud & DevOps: Azure DevOps, Agile (Azure Boards)
• AI/ML Tools: Azure Databricks, MLFlow Model Lifecycle Management, Unity Catalog (Azure Databricks)
• Cloud Services: Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, Azure AI Search
• Productivity Tools: Microsoft Copilot Studio (basic)
• Good-to-Have Skills

Ops & Engineering

• AgentOps / LLMOps
• Agent monitoring, evaluation, and debugging frameworks.

LLM observability and tracing (LangSmith, LangFuse, Weights & Biases ).

Prompt/version management and experimentation.

• Governance, compliance, and cost optimization for LLMs.
• CI/CD pipelines in Azure DevOps.
• Flask, Docker.
• Other AI/ML Skills
• Document digitization and OCR methods.
• Azure Document Intelligence or equivalent.
• Azure Delta Lake.
• Behavioral Competencies
• Flexible to contribute to ad-hoc initiatives such as PoCs, solution prototyping, and proposal workflows.
• Open to working on non-GenAI AI/ML projects (e.g., computer vision, document digitization, data structuring, brainstorming for business use cases).
• Proactive in providing timely updates and driving tasks to completion.
• Demonstrates responsibility, accountability, curiosity, and an innovative mindset.
• Willingness to learn and understand the business context (e.g., Philips domain and data landscape) beyond core technical skills.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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